Building an Open Science Framework to Model Soil Organic Carbon

Abstract

As funding agencies embrace open science principles that encourage sharing data and computer code developed to produce research outputs, we must respond with new modes of publication. Furthermore, as we address the expanding reproducibility crisis in the sciences, we must work to release research materials in ways that enable reproducibility-publishing data, computer code, and research products in addition to the traditional journal article. Toward addressing these needs, we present an example framework to model and map soil organic carbon (SOC) in the cereal grains production region of the northwestern United States. Primarily associated with soil organic matter, SOC relates to many soil properties that influence resiliency and soil health for agriculture. It is also critical for understanding soil-atmospheric C flux, a significant part of the overall C budget of the Earth. The technique for modeling soil properties uses seven categories of environmental input data to make predictions: known soil attributes, climatic values, organisms present, relief, parent material, age, and spatial location. We gather data representing these categories from various public sources. The map is produced using a random forest statistical model with inputs to predict SOC content on a 30-m spatial grid. All modeling components including input data, metadata, computer code, and output products are made freely available under an explicit open-source license. In this way, reproducibility is supported, the methods and code released are available to be reused by other researchers, and the research products are open to critical review and improvement.

Measurements and Models to Identify Agroecosystem Practices That Enhance Soil Organic Carbon under Changing Climate

“…Knowledge of current SOC stocks across broader landscapes and regions, and of the relationships between SOC stocks and a range of soil, vegetation, climate, and other physiographic features, is essential for establishing baseline conditions and for assessing responses to future changes in climate and other disturbances. This section highlights several advances in the application of statistical models, as well as measurement techniques (e.g., MIR spectroscopy used by Vågen et al, 2018) and "big data" approaches (Flathers and Gessler, 2018), which will facilitate the mapping of SOC stocks at landscape to national scales.…”

“…Four papers in the section (summarized below) applied statistical models to develop maps of current SOC stocks across different geographical regions and spatial scales, including a 15‐km 2 area in southeastern Brazil (Costa et al, 2018), two different 100‐km 2 regions in South Africa (Vågen et al, 2018), a 100,000‐km 2 region of the northwestern United States (Flathers and Gessler, 2018), and a 150,000‐km 2 region in central Chile (Reyes Rojas et al, 2018). Two of these efforts (Flathers and Gessler, 2018; Reyes Rojas et al, 2018) used random forest statistics with the scorpan modeling approach of McBratney et al (2003), which uses seven categories of input data to make SOC predictions: known soil attributes, climatic values, organisms present, relief, parent material, age, and spatial location. In addition to assessing current SOC stocks, Reyes Rojas et al (2018) extended their effort to assessing future climate effects.…”

“…The relationships among SOC, SOM fractions, and environmental covariates were affected by local landscape variability. Flathers and Gessler (2018) applied the scorpan technique for modeling soil properties and as an example framework to model and map SOC stocks in the cereal grains production region of the northwestern United States. The map was produced using a random forest statistical model with scorpan inputs to predict SOC content on a 30-m spatial grid.…”

Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework

“…Multi-discliplinarity in virtual modeling & simulation In addition to the traditional tools mentioned earlier, contemporary soil-monitoring and -mapping techniques encompass advanced tools that provide estimations of carbon sequestration or carbon stock [42][43][44], the simulation of climatic scenarios or land use changes [45], and other simulations crucial for understanding soil dynamics [46]. One such tool is the RothC model [47] employing carbon models and soil-carbon-mapping techniques, integrating field data, remote sensing data, and computational algorithms to estimate soil organic carbon stocks and changes over time.…”

Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates

“…The use of soil modelling techniques is becoming more and more relevant for multiple disciplines related to soil sciences such as environmental science, soil geography, agronomy, ecology, and land management (Flathers and Gessler 2018;Rodrigo-Comino et al 2018;van Leeuwen et al 2019). This is because collecting a representative number of soil samples at a large scale is time-consuming and costly.…”